Hearing devices are wearable hearing apparatuses used to assist those with impaired hearing. To meet the numerous individual requirements different designs of hearing device are provided, such as behind-the-ear (BTE) hearing devices, receiver-in-the-canal (RIC) hearing devices, in-the-ear (ITE) hearing devices and also Concha or in-canal (ITE, CIC) hearing devices. The typical hearing devices mentioned are worn on the outer ear or in the auditory canal. Above and beyond these designs however there are also bone conduction hearing aids, implantable or vibro-tactile hearing aids available on the market. In such hearing aids the damaged hearing is simulated either mechanically or electrically.
Hearing devices principally have as their main components an input converter, an amplifier and an output converter. The input converter is as a rule a sound receiver, e.g. a microphone, and/or an electromagnetic receiver, e.g. an induction coil. The output converter is mostly implemented as an electroacoustic converter, e.g. a miniature loudspeaker or as an electromechanical converter, e.g. bone conduction earpiece. The amplifier is usually integrated into a signal processing unit. This basic structure is shown in FIG. 1, using a behind-the-ear hearing device as an example. One or more microphones 2 for recording the sound from the surroundings are built into a hearing device housing 1 worn behind the ear. A signal processing unit 3, which is also integrated into the hearing device housing 1, processes the microphone signals and amplifies them. The output signal of the signal processing unit 3 is transmitted to a loudspeaker or earpiece 4 which outputs an acoustic signal. The sound is transmitted, if necessary via a sound tube, which is fixed with an otoplastic in the auditory canal, to the hearing device wearer's eardrum. The power is supplied to the hearing device and especially to the signal processing unit 3 by a battery 5 also integrated into the hearing device housing 1.
In the processing of digital speech recording, e.g. digital hearing devices, it is often desirable to suppress disruptive background noise without influencing the useful signal (speech). There are known filter methods suitable for this purpose which influence the short-term spectrum of the signal, such as the Wiener filter. However these methods require a precise estimation of the frequency-dependent power of the noise to be suppressed from an input signal. If this estimation is imprecise, either an unsatisfactory noise suppression is achieved, the desired signal is affected or additional artificially-created noise signals, so called “musical tones” occur. There are no methods for noise estimation yet available which solve these problems completely and efficiently.
Previously noise power has been able to be estimated principally using two approaches. Both methods can be undertaken either over a wide bandwidth or preferably in a frequency range split up by means of a filter bank or short-term Fourier transformation:
1. Speech Activity Detection:
Provided no speech activity is detected, the complete (time-variable) input signal power is regarded as noise. If speech activity is detected, the noise estimation is kept constant at the last value before the onset of the speech activity.
2. Noise Power Estimation During Speech Activity (the so Called “Minimum Tracking Method”):
It is known that during speech activity the speech signal power in individual frequency ranges is repeatedly briefly almost zero. If there is now an underlying mixture of speech and noise changing comparatively slowly over time, the minima of the spectral signal power considered over time correspond to the noise power at these times. The noise signal power must lie between the established minima (minimum tracking). Such a minimum tracking can for example be performed with the aid of a smoothing filter, which is described for example in R. Martin, “Noise power spectral density estimation based on optimal smoothing and minimum statistics”, IEEE Trans. Speech Audio Processing, Vol. 5, July 2001, Pages 504-512 or S. Rangachari, P. Loizou, “A noise-estimation algorithm for highly non-stationary environments”, Speech Communication, Vol. 48, February 2006, Pages 220-231. The noise power is typically determined separately for different frequency ranges in the input signal. To this end the input signal is first split up by means of a filter bank or a Fourier transformation into individual frequency components. These components are then processed separately from one another.